We use SubTB(\(\lambda \) ) to train models on the molecule generation task of [1]}. The task is to generate binders of the sEH (soluble epoxide hydrolase) protein, based on a docking prediction [2]}. To be precise, molecules are generated by sequentially joining `blocks' from a fixed library to the partial molecular graph [3]}, [4]} The reward function \(R\)  is given by a pretrained proxy model made available by [1]}. To adjust the greediness of the agent, an inverse temperature hyperparameter \(\beta \)  is used, i.e., the reward used for training is \(R(x) = \widetilde{R}(x)^\beta \) , where \(\widetilde{R}(x)\)  is the proxy's prediction.
